To train CaFi-Net, we augment NeRF density fields with random rotations \(R_{rand}\)  and canonicalize them at each training iteration. We can easily do this by sampling the NeRF model at location \(R_{rand}^{-1}x\)  instead of \(x\)  in a differentiable manner using [1]}. Our method is built over equivariant layers and non-linearities from [2]}. tab:architecture shows the non-linearities and learning layers that are used after each equivariant convolution described in the main manuscript.
